In spite of its enormous successes, structural biology is severely limited by the low success rates of macromolecular crystallization, and by the poor performance of many samples in NMR. Many valuable, biomedically important targets elude crystallization attempts, and overall high costs of structure determination are due primarily to the labor and time-intensive screening of targets at the stages of protein production, crystallization and initial HSQC spectra collection. Our research will address this bottleneck, through development of protein engineering strategies that allow for rational design of target variants with enhanced solubility, stability, and crystallizability or performance in NMR, and implementation of web-based, publicly available servers that facilitate application of these strategies. During the previous phase, we demonstrated that protein crystallization can be rationally induced by surface engineering based on the premise of surface entropy reduction (SER), i.e. mutagenesis of large, polar and solvent exposed amino acids, such as Lys, Glu and Gln, with small residues, e.g. Ala. Further, we designed and implemented the first generation XtalPred and SERp servers, which offer automated evaluation of protein's propensity to crystallize and design of variants with enhanced crystallizability based on the SER strategy. These tools have been used successfully by thousands of investigators world-wide, and helped solve nearly 170 crystal structures, including those of novel globular and membrane proteins, complexes and drug- targets in drug design pipelines. We now propose to pursue further experimental and computational studies of the relationships between physical chemistry of the protein surface and its solution properties. Specifically, we will investigate how surface entropy reduction affects protein solubility and stability, and how protein surface properties impact on the quality of heteronuclear NMR spectra. We will design and implement second generation XtalPred and SERp algorithms, with numerous new features, to achieve higher success rates for prediction of protein crystallizability and for design of variants with higher crystallizability, solubility, and with enhanced performance in NMR spectroscopy. The two second generation servers will be integrated into an interoperable network with the fold-prediction server FFAS03, and we will design a Protein Construct Design Metaserver (PCDM) making it possible to access the entire toolbox from a single GUI, proceeding from prediction to interactive design of primers and/or synthetic genes for expression of targets. Finally, to validate the methods, we will test them using a selection of biologically relevant protein targets.